code: def addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None):
      if len(ivy.shape(batch1)) != 3 or len(ivy.shape(batch2)) != 3:
          raise RuntimeError("input must be 3D matrices")
      batch1, batch2 = torch_frontend.promote_types_of_torch_inputs(
          batch1, batch2
      )
      ret = ivy.matmul(batch1, batch2, out=out)
      ret = ivy.sum(
          ret, axis=0, keepdims=False, dtype=ivy.dtype(ret), out=out
      )
      alpha, ret = torch_frontend.promote_types_of_torch_inputs(
          alpha, ret
      )
      ret = ivy.multiply(alpha, ret, out=out)
      beta, input = torch_frontend.promote_types_of_torch_inputs(
          beta, input
      )
      beta_input = ivy.multiply(beta, input, out=out)
      beta_input, ret = torch_frontend.promote_types_of_torch_inputs(
          beta_input, ret
      )
      return ivy.add(beta_input, ret, out=out)
  
imports: import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  import ivy.functional.frontends.torch
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 1 for calling a global function with example usage: addbmm(input, batch1, batch2) and returns: ivy